In [4]:
print(dfmask['mask'].value_counts())
dfmask['mask'].value_counts().plot(kind='bar')
plt.show()
0 2556 1 1373 Name: mask, dtype: int64
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import zipfile
import cv2
from skimage import io
import tensorflow as tf
from tensorflow.python.keras import Sequential
from tensorflow.keras import layers, optimizers
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.utils import plot_model
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, LearningRateScheduler
from IPython.display import display
from tensorflow.keras import backend as K
from sklearn.preprocessing import StandardScaler, normalize
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import warnings
warnings.filterwarnings("ignore")
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/kaggle/input/brain-mri-detection-and-segmentation/TCGA_DU_7306_19930512/TCGA_DU_7306_19930512_10_mask.tif /kaggle/input/brain-mri-detection-and-segmentation/TCGA_DU_7306_19930512/TCGA_DU_7306_19930512_2.tif /kaggle/input/brain-mri-detection-and-segmentation/TCGA_DU_7306_19930512/TCGA_DU_7306_19930512_38_mask.tif
import pandas as pd
import numpy as np
import seaborn as sns
import cv2
import tensorflow as tf
import os
from skimage import io
from PIL import Image
from tensorflow.keras import backend as K
#creating a custom datagenerator:
class DataGenerator(tf.keras.utils.Sequence):
def __init__(self, ids , mask, image_dir = '/kaggle/input/brain-mri-detection-and-segmentation/', batch_size = 16, img_h = 256, img_w = 256, shuffle = True):
self.ids = ids
self.mask = mask
self.image_dir = image_dir
self.batch_size = batch_size
self.img_h = img_h
self.img_w = img_w
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Get the number of batches per epoch'
return int(np.floor(len(self.ids)) / self.batch_size)
def __getitem__(self, index):
'Generate a batch of data'
#generate index of batch_size length
indexes = self.indexes[index* self.batch_size : (index+1) * self.batch_size]
#get the ImageId corresponding to the indexes created above based on batch size
list_ids = [self.ids[i] for i in indexes]
#get the MaskId corresponding to the indexes created above based on batch size
list_mask = [self.mask[i] for i in indexes]
#generate data for the X(features) and y(label)
X, y = self.__data_generation(list_ids, list_mask)
#returning the data
return X, y
def on_epoch_end(self):
'Used for updating the indices after each epoch, once at the beginning as well as at the end of each epoch'
#getting the array of indices based on the input dataframe
self.indexes = np.arange(len(self.ids))
#if shuffle is true, shuffle the indices
if self.shuffle:
np.random.shuffle(self.indexes)
def __data_generation(self, list_ids, list_mask):
'generate the data corresponding the indexes in a given batch of images'
# create empty arrays of shape (batch_size,height,width,depth)
#Depth is 3 for input and depth is taken as 1 for output becasue mask consist only of 1 channel.
X = np.empty((self.batch_size, self.img_h, self.img_w, 3))
y = np.empty((self.batch_size, self.img_h, self.img_w, 1))
#iterate through the dataframe rows, whose size is equal to the batch_size
for i in range(len(list_ids)):
#path of the image
img_path = '/kaggle/input/brain-mri-detection-and-segmentation/' + str(list_ids[i])
#mask path
mask_path = '/kaggle/input/brain-mri-detection-and-segmentation/' + str(list_mask[i])
#reading the original image and the corresponding mask image
img = io.imread(img_path)
mask = io.imread(mask_path)
#resizing and coverting them to array of type float64
img = cv2.resize(img,(self.img_h,self.img_w))
img = np.array(img, dtype = np.float64)
mask = cv2.resize(mask,(self.img_h,self.img_w))
mask = np.array(mask, dtype = np.float64)
#standardising
img -= img.mean()
img /= img.std()
mask -= mask.mean()
mask /= mask.std()
#Adding image to the empty array
X[i,] = img
#expanding the dimnesion of the image from (256,256) to (256,256,1)
y[i,] = np.expand_dims(mask, axis = 2)
#normalizing y
y = (y > 0).astype(int)
return X, y
def prediction(test, model, model_seg):
'''
Predcition function which takes dataframe containing ImageID as Input and perform 2 type of prediction on the image
Initially, image is passed through the classification network which predicts whether the image has defect or not, if the model
is 99% sure that the image has no defect, then the image is labeled as no-defect, if the model is not sure, it passes the image to the
segmentation network, it again checks if the image has defect or not, if it has defect, then the type and location of defect is found
'''
#directory
directory = "/kaggle/input/brain-mri-detection-and-segmentation/"
#Creating empty list to store the results
mask = []
image_id = []
has_mask = []
#iterating through each image in the test data
for i in test.image_path:
path = directory + str(i)
#reading the image
img = io.imread(path)
#Normalizing the image
img = img * 1./255.
#Reshaping the image
img = cv2.resize(img,(256,256))
#Converting the image into array
img = np.array(img, dtype = np.float64)
#reshaping the image from 256,256,3 to 1,256,256,3
img = np.reshape(img, (1,256,256,3))
#making prediction on the image
is_defect = model.predict(img)
#if tumour is not present we append the details of the image to the list
if np.argmax(is_defect) == 0:
image_id.append(i)
has_mask.append(0)
mask.append('No mask')
continue
#Read the image
img = io.imread(path)
#Creating a empty array of shape 1,256,256,1
X = np.empty((1, 256, 256, 3))
#resizing the image and coverting them to array of type float64
img = cv2.resize(img,(256,256))
img = np.array(img, dtype = np.float64)
#standardising the image
img -= img.mean()
img /= img.std()
#converting the shape of image from 256,256,3 to 1,256,256,3
X[0,] = img
#make prediction
predict = model_seg.predict(X)
#if the sum of predicted values is equal to 0 then there is no tumour
if predict.round().astype(int).sum() == 0:
image_id.append(i)
has_mask.append(0)
mask.append('No mask')
else:
#if the sum of pixel values are more than 0, then there is tumour
image_id.append(i)
has_mask.append(1)
mask.append(predict)
return image_id, mask, has_mask
'''
We need a custom loss function to train this ResUNet.So, we have used the loss function as it is from https://github.com/nabsabraham/focal-tversky-unet/blob/master/losses.py
@article{focal-unet,
title={A novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation},
author={Abraham, Nabila and Khan, Naimul Mefraz},
journal={arXiv preprint arXiv:1810.07842},
year={2018}
}
'''
def tversky(y_true, y_pred, smooth = 1e-6):
y_true_pos = K.flatten(y_true)
y_pred_pos = K.flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos)
false_neg = K.sum(y_true_pos * (1-y_pred_pos))
false_pos = K.sum((1-y_true_pos)*y_pred_pos)
alpha = 0.7
return (true_pos + smooth)/(true_pos + alpha*false_neg + (1-alpha)*false_pos + smooth)
def tversky_loss(y_true, y_pred):
return 1 - tversky(y_true,y_pred)
def focal_tversky(y_true,y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(y_pred, tf.float32)
pt_1 = tversky(y_true, y_pred)
gamma = 0.75
return K.pow((1-pt_1), gamma)
dfmask = pd.read_csv('/kaggle/input/brain-mri-detection-and-segmentation/data_mask.csv')
dfmask.head()
| patient_id | image_path | mask_path | mask | |
|---|---|---|---|---|
| 0 | TCGA_CS_5395_19981004 | TCGA_CS_5395_19981004/TCGA_CS_5395_19981004_1.tif | TCGA_CS_5395_19981004/TCGA_CS_5395_19981004_1_... | 0 |
| 1 | TCGA_CS_5395_19981004 | TCGA_CS_4944_20010208/TCGA_CS_4944_20010208_1.tif | TCGA_CS_4944_20010208/TCGA_CS_4944_20010208_1_... | 0 |
| 2 | TCGA_CS_5395_19981004 | TCGA_CS_4941_19960909/TCGA_CS_4941_19960909_1.tif | TCGA_CS_4941_19960909/TCGA_CS_4941_19960909_1_... | 0 |
| 3 | TCGA_CS_5395_19981004 | TCGA_CS_4943_20000902/TCGA_CS_4943_20000902_1.tif | TCGA_CS_4943_20000902/TCGA_CS_4943_20000902_1_... | 0 |
| 4 | TCGA_CS_5395_19981004 | TCGA_CS_5396_20010302/TCGA_CS_5396_20010302_1.tif | TCGA_CS_5396_20010302/TCGA_CS_5396_20010302_1_... | 0 |
dfmask.dtypes
patient_id object image_path object mask_path object mask int64 dtype: object
print(dfmask['mask'].value_counts())
dfmask['mask'].value_counts().plot(kind='bar')
plt.show()
0 2556 1 1373 Name: mask, dtype: int64
import random
fig, axs = plt.subplots(6,2, figsize=(16,32))
count = 0
for x in range(6):
i = random.randint(0, len(dfmask))
axs[count][0].title.set_text("Brain MRI")
axs[count][0].imshow(cv2.imread('/kaggle/input/brain-mri-detection-and-segmentation/'+dfmask.image_path[i]))
axs[count][1].title.set_text("Mask = " + str(dfmask['mask'][i]))
axs[count][1].imshow(cv2.imread('/kaggle/input/brain-mri-detection-and-segmentation/'+dfmask.mask_path[i]))
count += 1
fig.tight_layout()
count = 0
fig, axs = plt.subplots(12, 3, figsize = (20, 50))
for i in range(len(dfmask)):
if dfmask['mask'][i] ==1 and count <12:
img = io.imread('/kaggle/input/brain-mri-detection-and-segmentation/'+dfmask.image_path[i])
axs[count][0].title.set_text('Brain MRI')
axs[count][0].imshow(img)
mask = io.imread('/kaggle/input/brain-mri-detection-and-segmentation/'+dfmask.mask_path[i])
axs[count][1].title.set_text('Mask')
axs[count][1].imshow(mask, cmap = 'gray')
img[mask == 255] = (255, 0, 0)
axs[count][2].title.set_text('MRI + Mask')
axs[count][2].imshow(img)
count+=1
fig.tight_layout()
traindf = dfmask.drop('patient_id',axis=1)
traindf['mask'] = traindf['mask'].apply(lambda x: str(x))
traindf.shape
(3929, 3)
from sklearn.model_selection import train_test_split
train, test = train_test_split(traindf, test_size = 0.15)
train.shape,test.shape
((3339, 3), (590, 3))
from keras_preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(rescale=1./255., validation_split = 0.15)
train_generator=datagen.flow_from_dataframe(dataframe=train,directory= '/kaggle/input/brain-mri-detection-and-segmentation/',
x_col='image_path',y_col='mask',subset="training",batch_size=16,shuffle=True,
class_mode="categorical",target_size=(256,256))
valid_generator=datagen.flow_from_dataframe(dataframe=train,directory= '/kaggle/input/brain-mri-detection-and-segmentation/',
x_col='image_path',y_col='mask',subset="validation",batch_size=16,shuffle=True,
class_mode="categorical",target_size=(256,256))
test_datagen=ImageDataGenerator(rescale=1./255.)
test_generator=test_datagen.flow_from_dataframe(dataframe=test,directory= '/kaggle/input/brain-mri-detection-and-segmentation/',
x_col='image_path',y_col='mask',batch_size=16,shuffle=False,
class_mode='categorical',target_size=(256,256))
Found 2839 validated image filenames belonging to 2 classes. Found 500 validated image filenames belonging to 2 classes. Found 590 validated image filenames belonging to 2 classes.
basemodel = ResNet50(weights = 'imagenet', include_top = False, input_tensor = Input(shape=(256, 256, 3)))
basemodel.summary()
2022-02-15 17:01:51.203586: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:51.334694: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:51.335615: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:51.336929: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-02-15 17:01:51.337981: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:51.338748: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:51.339502: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:53.605798: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:53.606721: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:53.607454: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2022-02-15 17:01:53.608030: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94773248/94765736 [==============================] - 0s 0us/step
94781440/94765736 [==============================] - 0s 0us/step
Model: "resnet50"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 262, 262, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
conv1_conv (Conv2D) (None, 128, 128, 64) 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
conv1_bn (BatchNormalization) (None, 128, 128, 64) 256 conv1_conv[0][0]
__________________________________________________________________________________________________
conv1_relu (Activation) (None, 128, 128, 64) 0 conv1_bn[0][0]
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D) (None, 130, 130, 64) 0 conv1_relu[0][0]
__________________________________________________________________________________________________
pool1_pool (MaxPooling2D) (None, 64, 64, 64) 0 pool1_pad[0][0]
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D) (None, 64, 64, 64) 4160 pool1_pool[0][0]
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 64, 64, 64) 0 conv2_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D) (None, 64, 64, 64) 36928 conv2_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_2_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_2_relu (Activation (None, 64, 64, 64) 0 conv2_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_0_conv (Conv2D) (None, 64, 64, 256) 16640 pool1_pool[0][0]
__________________________________________________________________________________________________
conv2_block1_3_conv (Conv2D) (None, 64, 64, 256) 16640 conv2_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_3_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_add (Add) (None, 64, 64, 256) 0 conv2_block1_0_bn[0][0]
conv2_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_out (Activation) (None, 64, 64, 256) 0 conv2_block1_add[0][0]
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D) (None, 64, 64, 64) 16448 conv2_block1_out[0][0]
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 64, 64, 64) 0 conv2_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D) (None, 64, 64, 64) 36928 conv2_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_2_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_2_relu (Activation (None, 64, 64, 64) 0 conv2_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_3_conv (Conv2D) (None, 64, 64, 256) 16640 conv2_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_3_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_add (Add) (None, 64, 64, 256) 0 conv2_block1_out[0][0]
conv2_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_out (Activation) (None, 64, 64, 256) 0 conv2_block2_add[0][0]
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D) (None, 64, 64, 64) 16448 conv2_block2_out[0][0]
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 64, 64, 64) 0 conv2_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D) (None, 64, 64, 64) 36928 conv2_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_2_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_2_relu (Activation (None, 64, 64, 64) 0 conv2_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_3_conv (Conv2D) (None, 64, 64, 256) 16640 conv2_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_3_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_add (Add) (None, 64, 64, 256) 0 conv2_block2_out[0][0]
conv2_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_out (Activation) (None, 64, 64, 256) 0 conv2_block3_add[0][0]
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D) (None, 32, 32, 128) 32896 conv2_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 32, 32, 128) 0 conv3_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_2_relu (Activation (None, 32, 32, 128) 0 conv3_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_0_conv (Conv2D) (None, 32, 32, 512) 131584 conv2_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block1_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_add (Add) (None, 32, 32, 512) 0 conv3_block1_0_bn[0][0]
conv3_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_out (Activation) (None, 32, 32, 512) 0 conv3_block1_add[0][0]
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D) (None, 32, 32, 128) 65664 conv3_block1_out[0][0]
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 32, 32, 128) 0 conv3_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_2_relu (Activation (None, 32, 32, 128) 0 conv3_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_add (Add) (None, 32, 32, 512) 0 conv3_block1_out[0][0]
conv3_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_out (Activation) (None, 32, 32, 512) 0 conv3_block2_add[0][0]
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D) (None, 32, 32, 128) 65664 conv3_block2_out[0][0]
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 32, 32, 128) 0 conv3_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_2_relu (Activation (None, 32, 32, 128) 0 conv3_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_add (Add) (None, 32, 32, 512) 0 conv3_block2_out[0][0]
conv3_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_out (Activation) (None, 32, 32, 512) 0 conv3_block3_add[0][0]
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D) (None, 32, 32, 128) 65664 conv3_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 32, 32, 128) 0 conv3_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_2_relu (Activation (None, 32, 32, 128) 0 conv3_block4_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block4_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block4_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_add (Add) (None, 32, 32, 512) 0 conv3_block3_out[0][0]
conv3_block4_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_out (Activation) (None, 32, 32, 512) 0 conv3_block4_add[0][0]
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D) (None, 16, 16, 256) 131328 conv3_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 16, 16, 256) 0 conv4_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_2_relu (Activation (None, 16, 16, 256) 0 conv4_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_0_conv (Conv2D) (None, 16, 16, 1024) 525312 conv3_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block1_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_add (Add) (None, 16, 16, 1024) 0 conv4_block1_0_bn[0][0]
conv4_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_out (Activation) (None, 16, 16, 1024) 0 conv4_block1_add[0][0]
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block1_out[0][0]
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 16, 16, 256) 0 conv4_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_2_relu (Activation (None, 16, 16, 256) 0 conv4_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_add (Add) (None, 16, 16, 1024) 0 conv4_block1_out[0][0]
conv4_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_out (Activation) (None, 16, 16, 1024) 0 conv4_block2_add[0][0]
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block2_out[0][0]
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 16, 16, 256) 0 conv4_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_2_relu (Activation (None, 16, 16, 256) 0 conv4_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_add (Add) (None, 16, 16, 1024) 0 conv4_block2_out[0][0]
conv4_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_out (Activation) (None, 16, 16, 1024) 0 conv4_block3_add[0][0]
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block3_out[0][0]
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 16, 16, 256) 0 conv4_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_2_relu (Activation (None, 16, 16, 256) 0 conv4_block4_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block4_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block4_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_add (Add) (None, 16, 16, 1024) 0 conv4_block3_out[0][0]
conv4_block4_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_out (Activation) (None, 16, 16, 1024) 0 conv4_block4_add[0][0]
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 16, 16, 256) 0 conv4_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_2_relu (Activation (None, 16, 16, 256) 0 conv4_block5_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block5_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block5_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_add (Add) (None, 16, 16, 1024) 0 conv4_block4_out[0][0]
conv4_block5_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_out (Activation) (None, 16, 16, 1024) 0 conv4_block5_add[0][0]
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block5_out[0][0]
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 16, 16, 256) 0 conv4_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block6_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_2_relu (Activation (None, 16, 16, 256) 0 conv4_block6_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block6_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block6_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_add (Add) (None, 16, 16, 1024) 0 conv4_block5_out[0][0]
conv4_block6_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_out (Activation) (None, 16, 16, 1024) 0 conv4_block6_add[0][0]
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D) (None, 8, 8, 512) 524800 conv4_block6_out[0][0]
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 8, 8, 512) 0 conv5_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D) (None, 8, 8, 512) 2359808 conv5_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 8, 8, 512) 0 conv5_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D) (None, 8, 8, 2048) 2099200 conv4_block6_out[0][0]
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D) (None, 8, 8, 2048) 1050624 conv5_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_3_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_add (Add) (None, 8, 8, 2048) 0 conv5_block1_0_bn[0][0]
conv5_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_out (Activation) (None, 8, 8, 2048) 0 conv5_block1_add[0][0]
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D) (None, 8, 8, 512) 1049088 conv5_block1_out[0][0]
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 8, 8, 512) 0 conv5_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D) (None, 8, 8, 512) 2359808 conv5_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 8, 8, 512) 0 conv5_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D) (None, 8, 8, 2048) 1050624 conv5_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_3_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_add (Add) (None, 8, 8, 2048) 0 conv5_block1_out[0][0]
conv5_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_out (Activation) (None, 8, 8, 2048) 0 conv5_block2_add[0][0]
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D) (None, 8, 8, 512) 1049088 conv5_block2_out[0][0]
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 8, 8, 512) 0 conv5_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D) (None, 8, 8, 512) 2359808 conv5_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 8, 8, 512) 0 conv5_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D) (None, 8, 8, 2048) 1050624 conv5_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_add (Add) (None, 8, 8, 2048) 0 conv5_block2_out[0][0]
conv5_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_out (Activation) (None, 8, 8, 2048) 0 conv5_block3_add[0][0]
==================================================================================================
Total params: 23,587,712
Trainable params: 23,534,592
Non-trainable params: 53,120
__________________________________________________________________________________________________
for layer in basemodel.layers:
layers.trainable = False
headmodel = basemodel.output
headmodel = AveragePooling2D(pool_size = (4,4))(headmodel)
headmodel = Flatten(name= 'flatten')(headmodel)
headmodel = Dense(256, activation = "relu")(headmodel)
headmodel = Dropout(0.3)(headmodel)
headmodel = Dense(256, activation = "relu")(headmodel)
headmodel = Dropout(0.3)(headmodel)
headmodel = Dense(2, activation = 'softmax')(headmodel)
model = Model(inputs = basemodel.input, outputs = headmodel)
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D) (None, 262, 262, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
conv1_conv (Conv2D) (None, 128, 128, 64) 9472 conv1_pad[0][0]
__________________________________________________________________________________________________
conv1_bn (BatchNormalization) (None, 128, 128, 64) 256 conv1_conv[0][0]
__________________________________________________________________________________________________
conv1_relu (Activation) (None, 128, 128, 64) 0 conv1_bn[0][0]
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D) (None, 130, 130, 64) 0 conv1_relu[0][0]
__________________________________________________________________________________________________
pool1_pool (MaxPooling2D) (None, 64, 64, 64) 0 pool1_pad[0][0]
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D) (None, 64, 64, 64) 4160 pool1_pool[0][0]
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 64, 64, 64) 0 conv2_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D) (None, 64, 64, 64) 36928 conv2_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_2_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_2_relu (Activation (None, 64, 64, 64) 0 conv2_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_0_conv (Conv2D) (None, 64, 64, 256) 16640 pool1_pool[0][0]
__________________________________________________________________________________________________
conv2_block1_3_conv (Conv2D) (None, 64, 64, 256) 16640 conv2_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_3_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block1_add (Add) (None, 64, 64, 256) 0 conv2_block1_0_bn[0][0]
conv2_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block1_out (Activation) (None, 64, 64, 256) 0 conv2_block1_add[0][0]
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D) (None, 64, 64, 64) 16448 conv2_block1_out[0][0]
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 64, 64, 64) 0 conv2_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D) (None, 64, 64, 64) 36928 conv2_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_2_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_2_relu (Activation (None, 64, 64, 64) 0 conv2_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_3_conv (Conv2D) (None, 64, 64, 256) 16640 conv2_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block2_3_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block2_add (Add) (None, 64, 64, 256) 0 conv2_block1_out[0][0]
conv2_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block2_out (Activation) (None, 64, 64, 256) 0 conv2_block2_add[0][0]
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D) (None, 64, 64, 64) 16448 conv2_block2_out[0][0]
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 64, 64, 64) 0 conv2_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D) (None, 64, 64, 64) 36928 conv2_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_2_bn (BatchNormali (None, 64, 64, 64) 256 conv2_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_2_relu (Activation (None, 64, 64, 64) 0 conv2_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_3_conv (Conv2D) (None, 64, 64, 256) 16640 conv2_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv2_block3_3_bn (BatchNormali (None, 64, 64, 256) 1024 conv2_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv2_block3_add (Add) (None, 64, 64, 256) 0 conv2_block2_out[0][0]
conv2_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv2_block3_out (Activation) (None, 64, 64, 256) 0 conv2_block3_add[0][0]
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D) (None, 32, 32, 128) 32896 conv2_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 32, 32, 128) 0 conv3_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_2_relu (Activation (None, 32, 32, 128) 0 conv3_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_0_conv (Conv2D) (None, 32, 32, 512) 131584 conv2_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block1_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block1_add (Add) (None, 32, 32, 512) 0 conv3_block1_0_bn[0][0]
conv3_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block1_out (Activation) (None, 32, 32, 512) 0 conv3_block1_add[0][0]
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D) (None, 32, 32, 128) 65664 conv3_block1_out[0][0]
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 32, 32, 128) 0 conv3_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_2_relu (Activation (None, 32, 32, 128) 0 conv3_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block2_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block2_add (Add) (None, 32, 32, 512) 0 conv3_block1_out[0][0]
conv3_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block2_out (Activation) (None, 32, 32, 512) 0 conv3_block2_add[0][0]
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D) (None, 32, 32, 128) 65664 conv3_block2_out[0][0]
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 32, 32, 128) 0 conv3_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_2_relu (Activation (None, 32, 32, 128) 0 conv3_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block3_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block3_add (Add) (None, 32, 32, 512) 0 conv3_block2_out[0][0]
conv3_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block3_out (Activation) (None, 32, 32, 512) 0 conv3_block3_add[0][0]
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D) (None, 32, 32, 128) 65664 conv3_block3_out[0][0]
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 32, 32, 128) 0 conv3_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D) (None, 32, 32, 128) 147584 conv3_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_2_bn (BatchNormali (None, 32, 32, 128) 512 conv3_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_2_relu (Activation (None, 32, 32, 128) 0 conv3_block4_2_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_3_conv (Conv2D) (None, 32, 32, 512) 66048 conv3_block4_2_relu[0][0]
__________________________________________________________________________________________________
conv3_block4_3_bn (BatchNormali (None, 32, 32, 512) 2048 conv3_block4_3_conv[0][0]
__________________________________________________________________________________________________
conv3_block4_add (Add) (None, 32, 32, 512) 0 conv3_block3_out[0][0]
conv3_block4_3_bn[0][0]
__________________________________________________________________________________________________
conv3_block4_out (Activation) (None, 32, 32, 512) 0 conv3_block4_add[0][0]
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D) (None, 16, 16, 256) 131328 conv3_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 16, 16, 256) 0 conv4_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_2_relu (Activation (None, 16, 16, 256) 0 conv4_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_0_conv (Conv2D) (None, 16, 16, 1024) 525312 conv3_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block1_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block1_add (Add) (None, 16, 16, 1024) 0 conv4_block1_0_bn[0][0]
conv4_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block1_out (Activation) (None, 16, 16, 1024) 0 conv4_block1_add[0][0]
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block1_out[0][0]
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 16, 16, 256) 0 conv4_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_2_relu (Activation (None, 16, 16, 256) 0 conv4_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block2_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block2_add (Add) (None, 16, 16, 1024) 0 conv4_block1_out[0][0]
conv4_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block2_out (Activation) (None, 16, 16, 1024) 0 conv4_block2_add[0][0]
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block2_out[0][0]
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 16, 16, 256) 0 conv4_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_2_relu (Activation (None, 16, 16, 256) 0 conv4_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block3_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block3_add (Add) (None, 16, 16, 1024) 0 conv4_block2_out[0][0]
conv4_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block3_out (Activation) (None, 16, 16, 1024) 0 conv4_block3_add[0][0]
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block3_out[0][0]
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block4_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 16, 16, 256) 0 conv4_block4_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block4_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block4_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_2_relu (Activation (None, 16, 16, 256) 0 conv4_block4_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block4_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block4_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block4_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block4_add (Add) (None, 16, 16, 1024) 0 conv4_block3_out[0][0]
conv4_block4_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block4_out (Activation) (None, 16, 16, 1024) 0 conv4_block4_add[0][0]
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block4_out[0][0]
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block5_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 16, 16, 256) 0 conv4_block5_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block5_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block5_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_2_relu (Activation (None, 16, 16, 256) 0 conv4_block5_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block5_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block5_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block5_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block5_add (Add) (None, 16, 16, 1024) 0 conv4_block4_out[0][0]
conv4_block5_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block5_out (Activation) (None, 16, 16, 1024) 0 conv4_block5_add[0][0]
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D) (None, 16, 16, 256) 262400 conv4_block5_out[0][0]
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block6_1_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 16, 16, 256) 0 conv4_block6_1_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D) (None, 16, 16, 256) 590080 conv4_block6_1_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_2_bn (BatchNormali (None, 16, 16, 256) 1024 conv4_block6_2_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_2_relu (Activation (None, 16, 16, 256) 0 conv4_block6_2_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_3_conv (Conv2D) (None, 16, 16, 1024) 263168 conv4_block6_2_relu[0][0]
__________________________________________________________________________________________________
conv4_block6_3_bn (BatchNormali (None, 16, 16, 1024) 4096 conv4_block6_3_conv[0][0]
__________________________________________________________________________________________________
conv4_block6_add (Add) (None, 16, 16, 1024) 0 conv4_block5_out[0][0]
conv4_block6_3_bn[0][0]
__________________________________________________________________________________________________
conv4_block6_out (Activation) (None, 16, 16, 1024) 0 conv4_block6_add[0][0]
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D) (None, 8, 8, 512) 524800 conv4_block6_out[0][0]
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block1_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 8, 8, 512) 0 conv5_block1_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D) (None, 8, 8, 512) 2359808 conv5_block1_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block1_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 8, 8, 512) 0 conv5_block1_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D) (None, 8, 8, 2048) 2099200 conv4_block6_out[0][0]
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D) (None, 8, 8, 2048) 1050624 conv5_block1_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block1_0_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_3_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block1_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block1_add (Add) (None, 8, 8, 2048) 0 conv5_block1_0_bn[0][0]
conv5_block1_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block1_out (Activation) (None, 8, 8, 2048) 0 conv5_block1_add[0][0]
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D) (None, 8, 8, 512) 1049088 conv5_block1_out[0][0]
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block2_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 8, 8, 512) 0 conv5_block2_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D) (None, 8, 8, 512) 2359808 conv5_block2_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block2_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 8, 8, 512) 0 conv5_block2_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D) (None, 8, 8, 2048) 1050624 conv5_block2_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block2_3_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block2_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block2_add (Add) (None, 8, 8, 2048) 0 conv5_block1_out[0][0]
conv5_block2_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block2_out (Activation) (None, 8, 8, 2048) 0 conv5_block2_add[0][0]
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D) (None, 8, 8, 512) 1049088 conv5_block2_out[0][0]
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 8, 8, 512) 0 conv5_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D) (None, 8, 8, 512) 2359808 conv5_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 8, 8, 512) 2048 conv5_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 8, 8, 512) 0 conv5_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D) (None, 8, 8, 2048) 1050624 conv5_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 8, 8, 2048) 8192 conv5_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_add (Add) (None, 8, 8, 2048) 0 conv5_block2_out[0][0]
conv5_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_out (Activation) (None, 8, 8, 2048) 0 conv5_block3_add[0][0]
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 2, 2, 2048) 0 conv5_block3_out[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 8192) 0 average_pooling2d[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 256) 2097408 flatten[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 256) 0 dense[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 256) 65792 dropout[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 256) 0 dense_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 2) 514 dropout_1[0][0]
==================================================================================================
Total params: 25,751,426
Trainable params: 25,698,306
Non-trainable params: 53,120
__________________________________________________________________________________________________
model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics= ["accuracy"])
earlystopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)
checkpointer = ModelCheckpoint(filepath="classifier-resnet-weights.hdf5", verbose=1, save_best_only=True)
history = model.fit(train_generator, steps_per_epoch= train_generator.n // 16, epochs = 40, validation_data= valid_generator, validation_steps= valid_generator.n // 16, callbacks=[checkpointer, earlystopping])
2022-02-15 17:02:23.053344: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/40
2022-02-15 17:02:30.206362: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005
177/177 [==============================] - 41s 156ms/step - loss: 0.8418 - accuracy: 0.7411 - val_loss: 2.9274 - val_accuracy: 0.6472 Epoch 00001: val_loss improved from inf to 2.92740, saving model to classifier-resnet-weights.hdf5 Epoch 2/40 177/177 [==============================] - 24s 138ms/step - loss: 0.4340 - accuracy: 0.8296 - val_loss: 2.0411 - val_accuracy: 0.6512 Epoch 00002: val_loss improved from 2.92740 to 2.04110, saving model to classifier-resnet-weights.hdf5 Epoch 3/40 177/177 [==============================] - 24s 137ms/step - loss: 0.3286 - accuracy: 0.8898 - val_loss: 0.6660 - val_accuracy: 0.6492 Epoch 00003: val_loss improved from 2.04110 to 0.66599, saving model to classifier-resnet-weights.hdf5 Epoch 4/40 177/177 [==============================] - 24s 136ms/step - loss: 0.2598 - accuracy: 0.8998 - val_loss: 1.1925 - val_accuracy: 0.6512 Epoch 00004: val_loss did not improve from 0.66599 Epoch 5/40 177/177 [==============================] - 24s 136ms/step - loss: 0.2245 - accuracy: 0.9171 - val_loss: 0.4541 - val_accuracy: 0.7964 Epoch 00005: val_loss improved from 0.66599 to 0.45414, saving model to classifier-resnet-weights.hdf5 Epoch 6/40 177/177 [==============================] - 25s 138ms/step - loss: 0.2203 - accuracy: 0.9199 - val_loss: 0.4902 - val_accuracy: 0.8044 Epoch 00006: val_loss did not improve from 0.45414 Epoch 7/40 177/177 [==============================] - 24s 137ms/step - loss: 0.2443 - accuracy: 0.9139 - val_loss: 0.3992 - val_accuracy: 0.8871 Epoch 00007: val_loss improved from 0.45414 to 0.39917, saving model to classifier-resnet-weights.hdf5 Epoch 8/40 177/177 [==============================] - 24s 136ms/step - loss: 0.2184 - accuracy: 0.9284 - val_loss: 0.4651 - val_accuracy: 0.8589 Epoch 00008: val_loss did not improve from 0.39917 Epoch 9/40 177/177 [==============================] - 24s 135ms/step - loss: 0.1822 - accuracy: 0.9391 - val_loss: 0.3289 - val_accuracy: 0.9113 Epoch 00009: val_loss improved from 0.39917 to 0.32886, saving model to classifier-resnet-weights.hdf5 Epoch 10/40 177/177 [==============================] - 24s 137ms/step - loss: 0.1718 - accuracy: 0.9401 - val_loss: 0.3723 - val_accuracy: 0.9113 Epoch 00010: val_loss did not improve from 0.32886 Epoch 11/40 177/177 [==============================] - 24s 135ms/step - loss: 0.1570 - accuracy: 0.9469 - val_loss: 0.2544 - val_accuracy: 0.9274 Epoch 00011: val_loss improved from 0.32886 to 0.25438, saving model to classifier-resnet-weights.hdf5 Epoch 12/40 177/177 [==============================] - 24s 136ms/step - loss: 0.1386 - accuracy: 0.9522 - val_loss: 0.3946 - val_accuracy: 0.9012 Epoch 00012: val_loss did not improve from 0.25438 Epoch 13/40 177/177 [==============================] - 24s 135ms/step - loss: 0.1528 - accuracy: 0.9465 - val_loss: 0.3015 - val_accuracy: 0.8931 Epoch 00013: val_loss did not improve from 0.25438 Epoch 14/40 177/177 [==============================] - 24s 135ms/step - loss: 0.1175 - accuracy: 0.9632 - val_loss: 0.2662 - val_accuracy: 0.9052 Epoch 00014: val_loss did not improve from 0.25438 Epoch 15/40 177/177 [==============================] - 24s 136ms/step - loss: 0.1057 - accuracy: 0.9649 - val_loss: 0.2220 - val_accuracy: 0.9375 Epoch 00015: val_loss improved from 0.25438 to 0.22198, saving model to classifier-resnet-weights.hdf5 Epoch 16/40 177/177 [==============================] - 24s 137ms/step - loss: 0.1014 - accuracy: 0.9681 - val_loss: 0.2614 - val_accuracy: 0.9315 Epoch 00016: val_loss did not improve from 0.22198 Epoch 17/40 177/177 [==============================] - 25s 139ms/step - loss: 0.0926 - accuracy: 0.9713 - val_loss: 0.1938 - val_accuracy: 0.9355 Epoch 00017: val_loss improved from 0.22198 to 0.19381, saving model to classifier-resnet-weights.hdf5 Epoch 18/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0960 - accuracy: 0.9674 - val_loss: 0.3490 - val_accuracy: 0.8891 Epoch 00018: val_loss did not improve from 0.19381 Epoch 19/40 177/177 [==============================] - 24s 136ms/step - loss: 0.0888 - accuracy: 0.9699 - val_loss: 0.2251 - val_accuracy: 0.9294 Epoch 00019: val_loss did not improve from 0.19381 Epoch 20/40 177/177 [==============================] - 24s 135ms/step - loss: 0.0955 - accuracy: 0.9678 - val_loss: 0.4820 - val_accuracy: 0.9093 Epoch 00020: val_loss did not improve from 0.19381 Epoch 21/40 177/177 [==============================] - 24s 136ms/step - loss: 0.0861 - accuracy: 0.9724 - val_loss: 0.2226 - val_accuracy: 0.9335 Epoch 00021: val_loss did not improve from 0.19381 Epoch 22/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0741 - accuracy: 0.9756 - val_loss: 0.3299 - val_accuracy: 0.9375 Epoch 00022: val_loss did not improve from 0.19381 Epoch 23/40 177/177 [==============================] - 25s 140ms/step - loss: 0.0834 - accuracy: 0.9734 - val_loss: 0.3923 - val_accuracy: 0.9194 Epoch 00023: val_loss did not improve from 0.19381 Epoch 24/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0858 - accuracy: 0.9702 - val_loss: 0.2279 - val_accuracy: 0.9476 Epoch 00024: val_loss did not improve from 0.19381 Epoch 25/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0507 - accuracy: 0.9812 - val_loss: 0.2436 - val_accuracy: 0.9315 Epoch 00025: val_loss did not improve from 0.19381 Epoch 26/40 177/177 [==============================] - 25s 139ms/step - loss: 0.1009 - accuracy: 0.9674 - val_loss: 0.1988 - val_accuracy: 0.9456 Epoch 00026: val_loss did not improve from 0.19381 Epoch 27/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0615 - accuracy: 0.9809 - val_loss: 0.2943 - val_accuracy: 0.9375 Epoch 00027: val_loss did not improve from 0.19381 Epoch 28/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0617 - accuracy: 0.9812 - val_loss: 0.3667 - val_accuracy: 0.9355 Epoch 00028: val_loss did not improve from 0.19381 Epoch 29/40 177/177 [==============================] - 24s 136ms/step - loss: 0.0687 - accuracy: 0.9766 - val_loss: 0.2788 - val_accuracy: 0.9355 Epoch 00029: val_loss did not improve from 0.19381 Epoch 30/40 177/177 [==============================] - 24s 135ms/step - loss: 0.0623 - accuracy: 0.9798 - val_loss: 0.2689 - val_accuracy: 0.9315 Epoch 00030: val_loss did not improve from 0.19381 Epoch 31/40 177/177 [==============================] - 24s 136ms/step - loss: 0.0644 - accuracy: 0.9805 - val_loss: 0.2748 - val_accuracy: 0.9375 Epoch 00031: val_loss did not improve from 0.19381 Epoch 32/40 177/177 [==============================] - 24s 135ms/step - loss: 0.0557 - accuracy: 0.9830 - val_loss: 0.3282 - val_accuracy: 0.9315 Epoch 00032: val_loss did not improve from 0.19381 Epoch 33/40 177/177 [==============================] - 24s 137ms/step - loss: 0.0552 - accuracy: 0.9809 - val_loss: 0.2933 - val_accuracy: 0.9234 Epoch 00033: val_loss did not improve from 0.19381 Epoch 34/40 177/177 [==============================] - 24s 135ms/step - loss: 0.0890 - accuracy: 0.9717 - val_loss: 0.7166 - val_accuracy: 0.8750 Epoch 00034: val_loss did not improve from 0.19381 Epoch 35/40 177/177 [==============================] - 24s 136ms/step - loss: 0.0667 - accuracy: 0.9787 - val_loss: 0.3325 - val_accuracy: 0.9173 Epoch 00035: val_loss did not improve from 0.19381 Epoch 36/40 177/177 [==============================] - 24s 135ms/step - loss: 0.0730 - accuracy: 0.9798 - val_loss: 0.2429 - val_accuracy: 0.9355 Epoch 00036: val_loss did not improve from 0.19381 Epoch 37/40 177/177 [==============================] - 24s 136ms/step - loss: 0.0444 - accuracy: 0.9862 - val_loss: 0.2388 - val_accuracy: 0.9395 Epoch 00037: val_loss did not improve from 0.19381 Epoch 00037: early stopping
model_json = model.to_json()
with open("classifier-resnet-model.json","w") as json_file:
json_file.write(model_json)
with open('classifier-resnet-model.json', 'r') as json_file:
json_savedModel= json_file.read()
model = tf.keras.models.model_from_json(json_savedModel)
model.load_weights('classifier-resnet-weights.hdf5')
model.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics= ["accuracy"])
test_predict = model.predict(test_generator, steps = test_generator.n // 16, verbose =1)
test_predict.shape
36/36 [==============================] - 4s 98ms/step
(576, 2)
predict = []
for i in test_predict:
predict.append(str(np.argmax(i)))
predict = np.asarray(predict)
original = np.asarray(test['mask'])[:len(predict)]
len(original)
576
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report
accuracy = accuracy_score(original, predict)
print(f'Accuracy: {accuracy}\n')
print(classification_report(original,predict, labels = [0,1]))
matrix = confusion_matrix(original, predict)
sns.heatmap(matrix, annot=True, fmt="d", cmap='Blues', square=True)
plt.xlabel("predicted")
plt.ylabel("actual")
plt.show()
Accuracy: 0.9461805555555556
precision recall f1-score support
0 0.95 0.97 0.96 391
1 0.94 0.89 0.91 185
micro avg 0.95 0.95 0.95 576
macro avg 0.94 0.93 0.94 576
weighted avg 0.95 0.95 0.95 576
dftum = dfmask[dfmask['mask'] == 1]
dftum.shape
(1373, 4)
from sklearn.model_selection import train_test_split
X_train, X_val = train_test_split(dftum, test_size=0.15)
X_test, X_val = train_test_split(X_val, test_size=0.5)
train_ids = list(X_train.image_path)
train_mask = list(X_train.mask_path)
val_ids = list(X_val.image_path)
val_mask= list(X_val.mask_path)
training_generator = DataGenerator(train_ids,train_mask)
validation_generator = DataGenerator(val_ids,val_mask)
def resblock(X, f):
X_copy = X
X = Conv2D(f, kernel_size = (1,1) ,strides = (1,1),kernel_initializer ='he_normal')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Conv2D(f, kernel_size = (3,3), strides =(1,1), padding = 'same', kernel_initializer ='he_normal')(X)
X = BatchNormalization()(X)
X_copy = Conv2D(f, kernel_size = (1,1), strides =(1,1), kernel_initializer ='he_normal')(X_copy)
X_copy = BatchNormalization()(X_copy)
X = Add()([X,X_copy])
X = Activation('relu')(X)
return X
def upsample_concat(x, skip):
x = UpSampling2D((2,2))(x)
merge = Concatenate()([x, skip])
return merge
input_shape = (256,256,3)
X_input = Input(input_shape)
conv1_in = Conv2D(16,3,activation= 'relu', padding = 'same', kernel_initializer ='he_normal')(X_input)
conv1_in = BatchNormalization()(conv1_in)
conv1_in = Conv2D(16,3,activation= 'relu', padding = 'same', kernel_initializer ='he_normal')(conv1_in)
conv1_in = BatchNormalization()(conv1_in)
pool_1 = MaxPool2D(pool_size = (2,2))(conv1_in)
conv2_in = resblock(pool_1, 32)
pool_2 = MaxPool2D(pool_size = (2,2))(conv2_in)
conv3_in = resblock(pool_2, 64)
pool_3 = MaxPool2D(pool_size = (2,2))(conv3_in)
conv4_in = resblock(pool_3, 128)
pool_4 = MaxPool2D(pool_size = (2,2))(conv4_in)
conv5_in = resblock(pool_4, 256)
up_1 = upsample_concat(conv5_in, conv4_in)
up_1 = resblock(up_1, 128)
up_2 = upsample_concat(up_1, conv3_in)
up_2 = resblock(up_2, 64)
up_3 = upsample_concat(up_2, conv2_in)
up_3 = resblock(up_3, 32)
up_4 = upsample_concat(up_3, conv1_in)
up_4 = resblock(up_4, 16)
output = Conv2D(1, (1,1), padding = "same", activation = "sigmoid")(up_4)
model_seg = Model(inputs = X_input, outputs = output )
model_seg.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 256, 256, 16) 448 input_2[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 256, 256, 16) 64 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 256, 256, 16) 2320 batch_normalization[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 256, 256, 16) 64 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 128, 128, 16) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 128, 128, 32) 544 max_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 128, 128, 32) 128 conv2d_2[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, 128, 128, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 128, 128, 32) 9248 activation[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 128, 128, 32) 544 max_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 128, 128, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 128, 128, 32) 128 conv2d_4[0][0]
__________________________________________________________________________________________________
add (Add) (None, 128, 128, 32) 0 batch_normalization_3[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 128, 128, 32) 0 add[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 64, 64, 32) 0 activation_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 64, 64, 64) 2112 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 64, 64, 64) 256 conv2d_5[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 64, 64, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 64, 64, 64) 36928 activation_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 64, 64, 64) 2112 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 64, 64, 64) 256 conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 64, 64, 64) 256 conv2d_7[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 64, 64, 64) 0 batch_normalization_6[0][0]
batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 64, 64, 64) 0 add_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 32, 32, 64) 0 activation_3[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 128) 8320 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 128) 512 conv2d_8[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 32, 32, 128) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 32, 32, 128) 147584 activation_4[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 32, 32, 128) 8320 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 32, 32, 128) 512 conv2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 32, 32, 128) 512 conv2d_10[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 32, 32, 128) 0 batch_normalization_9[0][0]
batch_normalization_10[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 32, 32, 128) 0 add_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 16, 16, 128) 0 activation_5[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 16, 16, 256) 33024 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 16, 16, 256) 1024 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 16, 16, 256) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 16, 16, 256) 590080 activation_6[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 16, 16, 256) 33024 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 16, 16, 256) 1024 conv2d_12[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 16, 16, 256) 1024 conv2d_13[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 16, 16, 256) 0 batch_normalization_12[0][0]
batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 16, 16, 256) 0 add_3[0][0]
__________________________________________________________________________________________________
up_sampling2d (UpSampling2D) (None, 32, 32, 256) 0 activation_7[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 32, 32, 384) 0 up_sampling2d[0][0]
activation_5[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 32, 32, 128) 49280 concatenate[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 32, 32, 128) 512 conv2d_14[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 32, 32, 128) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 32, 32, 128) 147584 activation_8[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 32, 32, 128) 49280 concatenate[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 32, 32, 128) 512 conv2d_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 32, 32, 128) 512 conv2d_16[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 32, 32, 128) 0 batch_normalization_15[0][0]
batch_normalization_16[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 32, 32, 128) 0 add_4[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, 64, 64, 128) 0 activation_9[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 64, 64, 192) 0 up_sampling2d_1[0][0]
activation_3[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 64, 64, 64) 12352 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 64, 64, 64) 256 conv2d_17[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 64, 64, 64) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 64, 64, 64) 36928 activation_10[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 64, 64, 64) 12352 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 64, 64, 64) 256 conv2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 64, 64, 64) 256 conv2d_19[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 64, 64, 64) 0 batch_normalization_18[0][0]
batch_normalization_19[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 64, 64, 64) 0 add_5[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, 128, 128, 64) 0 activation_11[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 128, 128, 96) 0 up_sampling2d_2[0][0]
activation_1[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 128, 128, 32) 3104 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 128, 128, 32) 128 conv2d_20[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 128, 128, 32) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 128, 128, 32) 9248 activation_12[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 128, 128, 32) 3104 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 128, 128, 32) 128 conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 128, 128, 32) 128 conv2d_22[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 128, 128, 32) 0 batch_normalization_21[0][0]
batch_normalization_22[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 128, 128, 32) 0 add_6[0][0]
__________________________________________________________________________________________________
up_sampling2d_3 (UpSampling2D) (None, 256, 256, 32) 0 activation_13[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 256, 256, 48) 0 up_sampling2d_3[0][0]
batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 256, 256, 16) 784 concatenate_3[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 256, 256, 16) 64 conv2d_23[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 256, 256, 16) 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 256, 256, 16) 2320 activation_14[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 256, 256, 16) 784 concatenate_3[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 256, 256, 16) 64 conv2d_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 256, 256, 16) 64 conv2d_25[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 256, 256, 16) 0 batch_normalization_24[0][0]
batch_normalization_25[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 256, 256, 16) 0 add_7[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 256, 256, 1) 17 activation_15[0][0]
==================================================================================================
Total params: 1,210,513
Trainable params: 1,206,129
Non-trainable params: 4,384
__________________________________________________________________________________________________
adam = tf.keras.optimizers.Adam(lr = 0.05, epsilon = 0.1)
model_seg.compile(optimizer = adam, loss = focal_tversky, metrics = [tversky])
earlystopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)
checkpointer = ModelCheckpoint(filepath="/kaggle/working/segmentation-weights.hdf5", verbose=1, save_best_only=True)
os.chdir('/kaggle/input/brain-mri-detection-and-segmentation/')
history = model_seg.fit(training_generator , epochs = 50, validation_data = validation_generator, callbacks = [checkpointer, earlystopping])
Epoch 1/50 72/72 [==============================] - 12s 129ms/step - loss: 0.4096 - tversky: 0.6940 - val_loss: 0.5849 - val_tversky: 0.5102 Epoch 00001: val_loss improved from inf to 0.58490, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 2/50 72/72 [==============================] - 8s 115ms/step - loss: 0.3446 - tversky: 0.7566 - val_loss: 0.4359 - val_tversky: 0.6657 Epoch 00002: val_loss improved from 0.58490 to 0.43591, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 3/50 72/72 [==============================] - 8s 113ms/step - loss: 0.3232 - tversky: 0.7763 - val_loss: 0.4274 - val_tversky: 0.6714 Epoch 00003: val_loss improved from 0.43591 to 0.42744, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 4/50 72/72 [==============================] - 9s 123ms/step - loss: 0.2996 - tversky: 0.7978 - val_loss: 0.4516 - val_tversky: 0.6525 Epoch 00004: val_loss did not improve from 0.42744 Epoch 5/50 72/72 [==============================] - 8s 117ms/step - loss: 0.2619 - tversky: 0.8311 - val_loss: 0.3346 - val_tversky: 0.7649 Epoch 00005: val_loss improved from 0.42744 to 0.33460, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 6/50 72/72 [==============================] - 8s 115ms/step - loss: 0.2483 - tversky: 0.8427 - val_loss: 0.3290 - val_tversky: 0.7715 Epoch 00006: val_loss improved from 0.33460 to 0.32901, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 7/50 72/72 [==============================] - 9s 125ms/step - loss: 0.2448 - tversky: 0.8455 - val_loss: 0.3326 - val_tversky: 0.7687 Epoch 00007: val_loss did not improve from 0.32901 Epoch 8/50 72/72 [==============================] - 8s 111ms/step - loss: 0.2321 - tversky: 0.8563 - val_loss: 0.3199 - val_tversky: 0.7799 Epoch 00008: val_loss improved from 0.32901 to 0.31985, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 9/50 72/72 [==============================] - 8s 112ms/step - loss: 0.2213 - tversky: 0.8643 - val_loss: 0.2873 - val_tversky: 0.8067 Epoch 00009: val_loss improved from 0.31985 to 0.28734, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 10/50 72/72 [==============================] - 8s 115ms/step - loss: 0.2167 - tversky: 0.8681 - val_loss: 0.2634 - val_tversky: 0.8297 Epoch 00010: val_loss improved from 0.28734 to 0.26335, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 11/50 72/72 [==============================] - 9s 121ms/step - loss: 0.2017 - tversky: 0.8804 - val_loss: 0.2603 - val_tversky: 0.8326 Epoch 00011: val_loss improved from 0.26335 to 0.26029, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 12/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1901 - tversky: 0.8891 - val_loss: 0.2570 - val_tversky: 0.8350 Epoch 00012: val_loss improved from 0.26029 to 0.25697, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 13/50 72/72 [==============================] - 8s 111ms/step - loss: 0.1890 - tversky: 0.8906 - val_loss: 0.2586 - val_tversky: 0.8345 Epoch 00013: val_loss did not improve from 0.25697 Epoch 14/50 72/72 [==============================] - 8s 116ms/step - loss: 0.1882 - tversky: 0.8912 - val_loss: 0.2804 - val_tversky: 0.8135 Epoch 00014: val_loss did not improve from 0.25697 Epoch 15/50 72/72 [==============================] - 9s 123ms/step - loss: 0.1836 - tversky: 0.8941 - val_loss: 0.2625 - val_tversky: 0.8254 Epoch 00015: val_loss did not improve from 0.25697 Epoch 16/50 72/72 [==============================] - 8s 115ms/step - loss: 0.1770 - tversky: 0.8992 - val_loss: 0.2377 - val_tversky: 0.8508 Epoch 00016: val_loss improved from 0.25697 to 0.23765, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 17/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1774 - tversky: 0.8991 - val_loss: 0.2436 - val_tversky: 0.8465 Epoch 00017: val_loss did not improve from 0.23765 Epoch 18/50 72/72 [==============================] - 8s 112ms/step - loss: 0.1830 - tversky: 0.8948 - val_loss: 0.2798 - val_tversky: 0.8167 Epoch 00018: val_loss did not improve from 0.23765 Epoch 19/50 72/72 [==============================] - 9s 120ms/step - loss: 0.1822 - tversky: 0.8955 - val_loss: 0.2419 - val_tversky: 0.8489 Epoch 00019: val_loss did not improve from 0.23765 Epoch 20/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1667 - tversky: 0.9072 - val_loss: 0.2154 - val_tversky: 0.8698 Epoch 00020: val_loss improved from 0.23765 to 0.21540, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 21/50 72/72 [==============================] - 8s 115ms/step - loss: 0.1811 - tversky: 0.8962 - val_loss: 0.2855 - val_tversky: 0.8105 Epoch 00021: val_loss did not improve from 0.21540 Epoch 22/50 72/72 [==============================] - 8s 117ms/step - loss: 0.1812 - tversky: 0.8962 - val_loss: 0.2323 - val_tversky: 0.8562 Epoch 00022: val_loss did not improve from 0.21540 Epoch 23/50 72/72 [==============================] - 8s 115ms/step - loss: 0.1547 - tversky: 0.9164 - val_loss: 0.2489 - val_tversky: 0.8414 Epoch 00023: val_loss did not improve from 0.21540 Epoch 24/50 72/72 [==============================] - 8s 115ms/step - loss: 0.1456 - tversky: 0.9229 - val_loss: 0.2245 - val_tversky: 0.8622 Epoch 00024: val_loss did not improve from 0.21540 Epoch 25/50 72/72 [==============================] - 8s 115ms/step - loss: 0.1327 - tversky: 0.9318 - val_loss: 0.1913 - val_tversky: 0.8894 Epoch 00025: val_loss improved from 0.21540 to 0.19128, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 26/50 72/72 [==============================] - 9s 119ms/step - loss: 0.1269 - tversky: 0.9359 - val_loss: 0.2064 - val_tversky: 0.8776 Epoch 00026: val_loss did not improve from 0.19128 Epoch 27/50 72/72 [==============================] - 8s 112ms/step - loss: 0.1316 - tversky: 0.9327 - val_loss: 0.2059 - val_tversky: 0.8776 Epoch 00027: val_loss did not improve from 0.19128 Epoch 28/50 72/72 [==============================] - 8s 116ms/step - loss: 0.1276 - tversky: 0.9354 - val_loss: 0.1873 - val_tversky: 0.8925 Epoch 00028: val_loss improved from 0.19128 to 0.18730, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 29/50 72/72 [==============================] - 8s 117ms/step - loss: 0.1192 - tversky: 0.9411 - val_loss: 0.1929 - val_tversky: 0.8882 Epoch 00029: val_loss did not improve from 0.18730 Epoch 30/50 72/72 [==============================] - 9s 122ms/step - loss: 0.1213 - tversky: 0.9395 - val_loss: 0.1928 - val_tversky: 0.8878 Epoch 00030: val_loss did not improve from 0.18730 Epoch 31/50 72/72 [==============================] - 8s 113ms/step - loss: 0.1157 - tversky: 0.9433 - val_loss: 0.1941 - val_tversky: 0.8873 Epoch 00031: val_loss did not improve from 0.18730 Epoch 32/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1127 - tversky: 0.9454 - val_loss: 0.2403 - val_tversky: 0.8494 Epoch 00032: val_loss did not improve from 0.18730 Epoch 33/50 72/72 [==============================] - 8s 116ms/step - loss: 0.1099 - tversky: 0.9472 - val_loss: 0.1907 - val_tversky: 0.8898 Epoch 00033: val_loss did not improve from 0.18730 Epoch 34/50 72/72 [==============================] - 9s 121ms/step - loss: 0.1092 - tversky: 0.9476 - val_loss: 0.1914 - val_tversky: 0.8893 Epoch 00034: val_loss did not improve from 0.18730 Epoch 35/50 72/72 [==============================] - 8s 112ms/step - loss: 0.1056 - tversky: 0.9499 - val_loss: 0.1896 - val_tversky: 0.8909 Epoch 00035: val_loss did not improve from 0.18730 Epoch 36/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1042 - tversky: 0.9507 - val_loss: 0.1952 - val_tversky: 0.8859 Epoch 00036: val_loss did not improve from 0.18730 Epoch 37/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1059 - tversky: 0.9497 - val_loss: 0.2171 - val_tversky: 0.8688 Epoch 00037: val_loss did not improve from 0.18730 Epoch 38/50 72/72 [==============================] - 9s 121ms/step - loss: 0.1075 - tversky: 0.9487 - val_loss: 0.1831 - val_tversky: 0.8958 Epoch 00038: val_loss improved from 0.18730 to 0.18312, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 39/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1076 - tversky: 0.9484 - val_loss: 0.2600 - val_tversky: 0.8335 Epoch 00039: val_loss did not improve from 0.18312 Epoch 40/50 72/72 [==============================] - 8s 112ms/step - loss: 0.1160 - tversky: 0.9431 - val_loss: 0.1871 - val_tversky: 0.8928 Epoch 00040: val_loss did not improve from 0.18312 Epoch 41/50 72/72 [==============================] - 8s 114ms/step - loss: 0.1003 - tversky: 0.9532 - val_loss: 0.1762 - val_tversky: 0.9006 Epoch 00041: val_loss improved from 0.18312 to 0.17620, saving model to /kaggle/working/segmentation-weights.hdf5 Epoch 42/50 72/72 [==============================] - 9s 122ms/step - loss: 0.0978 - tversky: 0.9548 - val_loss: 0.1821 - val_tversky: 0.8966 Epoch 00042: val_loss did not improve from 0.17620 Epoch 43/50 72/72 [==============================] - 8s 114ms/step - loss: 0.0964 - tversky: 0.9556 - val_loss: 0.2007 - val_tversky: 0.8822 Epoch 00043: val_loss did not improve from 0.17620 Epoch 44/50 72/72 [==============================] - 8s 111ms/step - loss: 0.0937 - tversky: 0.9573 - val_loss: 0.1810 - val_tversky: 0.8973 Epoch 00044: val_loss did not improve from 0.17620 Epoch 45/50 72/72 [==============================] - 8s 114ms/step - loss: 0.0912 - tversky: 0.9588 - val_loss: 0.2170 - val_tversky: 0.8683 Epoch 00045: val_loss did not improve from 0.17620 Epoch 46/50 72/72 [==============================] - 9s 121ms/step - loss: 0.0913 - tversky: 0.9587 - val_loss: 0.2080 - val_tversky: 0.8754 Epoch 00046: val_loss did not improve from 0.17620 Epoch 47/50 72/72 [==============================] - 8s 115ms/step - loss: 0.0888 - tversky: 0.9603 - val_loss: 0.1806 - val_tversky: 0.8975 Epoch 00047: val_loss did not improve from 0.17620 Epoch 48/50 72/72 [==============================] - 8s 110ms/step - loss: 0.0869 - tversky: 0.9614 - val_loss: 0.1969 - val_tversky: 0.8845 Epoch 00048: val_loss did not improve from 0.17620 Epoch 49/50 72/72 [==============================] - 8s 113ms/step - loss: 0.0853 - tversky: 0.9623 - val_loss: 0.1837 - val_tversky: 0.8951 Epoch 00049: val_loss did not improve from 0.17620 Epoch 50/50 72/72 [==============================] - 9s 119ms/step - loss: 0.0859 - tversky: 0.9619 - val_loss: 0.1858 - val_tversky: 0.8937 Epoch 00050: val_loss did not improve from 0.17620
model_json = model_seg.to_json()
with open("/kaggle/working/ResUNet-model.json","w") as json_file:
json_file.write(model_json)
with open('/kaggle/working/ResUNet-model.json', 'r') as json_file:
json_savedModel= json_file.read()
# cargar la arquitectura del modelo
model_seg = tf.keras.models.model_from_json(json_savedModel)
model_seg.load_weights('/kaggle/working/segmentation-weights.hdf5')
adam = tf.keras.optimizers.Adam(lr = 0.05, epsilon = 0.1)
model_seg.compile(optimizer = adam, loss = focal_tversky, metrics = [tversky])
image_id, mask, has_mask = prediction(test, model, model_seg)
df_pred = pd.DataFrame({'image_path': image_id,'predicted_mask': mask,'has_mask': has_mask})
df_pred
| image_path | predicted_mask | has_mask | |
|---|---|---|---|
| 0 | TCGA_DU_6407_19860514/TCGA_DU_6407_19860514_40... | No mask | 0 |
| 1 | TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_59... | No mask | 0 |
| 2 | TCGA_HT_7473_19970826/TCGA_HT_7473_19970826_25... | No mask | 0 |
| 3 | TCGA_HT_7608_19940304/TCGA_HT_7608_19940304_4.tif | No mask | 0 |
| 4 | TCGA_DU_7010_19860307/TCGA_DU_7010_19860307_52... | No mask | 0 |
| ... | ... | ... | ... |
| 585 | TCGA_DU_8166_19970322/TCGA_DU_8166_19970322_10... | No mask | 0 |
| 586 | TCGA_DU_6399_19830416/TCGA_DU_6399_19830416_13... | [[[[8.6600164e-07], [5.429585e-07], [4.2730122... | 1 |
| 587 | TCGA_HT_8106_19970727/TCGA_HT_8106_19970727_14... | [[[[8.8975264e-07], [4.7185927e-07], [2.564762... | 1 |
| 588 | TCGA_DU_6408_19860521/TCGA_DU_6408_19860521_46... | [[[[3.8300766e-07], [1.04063915e-07], [1.75584... | 1 |
| 589 | TCGA_HT_8114_19981030/TCGA_HT_8114_19981030_8.tif | No mask | 0 |
590 rows × 3 columns
df_pred = test.merge(df_pred, on = 'image_path')
df_pred.head()
| image_path | mask_path | mask | predicted_mask | has_mask | |
|---|---|---|---|---|---|
| 0 | TCGA_DU_6407_19860514/TCGA_DU_6407_19860514_40... | TCGA_DU_6407_19860514/TCGA_DU_6407_19860514_40... | 0 | No mask | 0 |
| 1 | TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_59... | TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_59... | 0 | No mask | 0 |
| 2 | TCGA_HT_7473_19970826/TCGA_HT_7473_19970826_25... | TCGA_HT_7473_19970826/TCGA_HT_7473_19970826_25... | 0 | No mask | 0 |
| 3 | TCGA_HT_7608_19940304/TCGA_HT_7608_19940304_4.tif | TCGA_HT_7608_19940304/TCGA_HT_7608_19940304_4_... | 0 | No mask | 0 |
| 4 | TCGA_DU_7010_19860307/TCGA_DU_7010_19860307_52... | TCGA_DU_7010_19860307/TCGA_DU_7010_19860307_52... | 0 | No mask | 0 |
count = 0
fig, axs = plt.subplots(10, 5, figsize=(30, 50))
for i in range(len(df_pred)):
if df_pred['has_mask'][i] == 1 and count < 10:
img = io.imread(df_pred.image_path[i])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
axs[count][0].title.set_text("Brain MRI")
axs[count][0].imshow(img)
mask = io.imread(df_pred.mask_path[i])
axs[count][1].title.set_text("Original Mask")
axs[count][1].imshow(mask)
predicted_mask = np.asarray(df_pred.predicted_mask[i])[0].squeeze().round()
axs[count][2].title.set_text("Predicted Mask")
axs[count][2].imshow(predicted_mask)
img[mask == 255] = (255, 0, 0)
axs[count][3].title.set_text("MRI with original Mask")
axs[count][3].imshow(img)
img_ = io.imread(df_pred.image_path[i])
img_ = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)
img_[predicted_mask == 1] = (0, 255, 0)
axs[count][4].title.set_text("MRI with predicted Mask")
axs[count][4].imshow(img_)
count += 1
fig.tight_layout()
